{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Vn26lahhFJd"
   },
   "source": [
    "# EECS 498-007/598-005 Assignment 6-1: Variational AutoEncoders\n",
    "\n",
    "Before we start, please put your name and UMID in following format\n",
    "\n",
    ": Firstname LASTNAME, #00000000   //   e.g.) Justin JOHNSON, #12345678"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZsZYgVWdALCH"
   },
   "source": [
    "**Your Answer:**   \n",
    "Hello WORLD, #XXXXXXXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VJZ8AefthL95"
   },
   "source": [
    "\n",
    "# Variational Autoencoder\n",
    "\n",
    "In this notebook, you will implement a variational autoencoder and a conditional variational autoencoder with slightly different architectures and apply them to the popular MNIST handwritten dataset. Recall from lecture (https://web.eecs.umich.edu/~justincj/slides/eecs498/FA2020/598_FA2020_lecture19.pdf), an autoencoder seeks to learn a latent representation of our training images by using unlabeled data and learning to reconstruct its inputs. The *variational autoencoder* extends this model by adding a probabilistic spin to the encoder and decoder, allowing us to sample from the learned distribution of the latent space to generate new images at inference time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JtA1_PsYhs24"
   },
   "source": [
    "## Setup Code\n",
    "Before getting started, we need to run some boilerplate code to set up our environment, same as previous assignments. You'll need to rerun this setup code each time you start the notebook.\n",
    "\n",
    "First, run this cell load the autoreload extension. This allows us to edit .py source files, and re-import them into the notebook for a seamless editing and debugging experience.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OKXSEQjRh63r"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eIXWSou6h_S6"
   },
   "source": [
    "### Google Colab Setup\n",
    "Next we need to run a few commands to set up our environment on Google Colab. If you are running this notebook on a local machine you can skip this section.\n",
    "\n",
    "Run the following cell to mount your Google Drive. Follow the link, sign in to your Google account (the same account you used to store this notebook!) and copy the authorization code into the text box that appears below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "evqEGDXRipC-"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2y_JbZTlDoai"
   },
   "source": [
    "Now recall the path in your Google Drive where you uploaded this notebook, fill it in below. If everything is working correctly then running the folowing cell should print the filenames from the assignment:\n",
    "\n",
    "```\n",
    "['eecs598', 'gan.py', 'generative_adversarial_networks.ipynb', 'a6_helper.py', 'vae.py', 'variational_autoencoders.ipynb']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z8OU2pRkivyc"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# TODO: Fill in the Google Drive path where you uploaded the assignment\n",
    "# Example: If you create a 2020FA folder and put all the files under A6 folder, then '2020FA/A6'\n",
    "GOOGLE_DRIVE_PATH_AFTER_MYDRIVE = None\n",
    "GOOGLE_DRIVE_PATH = os.path.join('drive', 'My Drive', GOOGLE_DRIVE_PATH_AFTER_MYDRIVE)\n",
    "print(os.listdir(GOOGLE_DRIVE_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GJ7auXOMi4rw"
   },
   "source": [
    "Once you have successfully mounted your Google Drive and located the path to \n",
    "this assignment, run the following cell to allow us to import from the `.py` files of this assignment. If it works correctly, it should print the message:\n",
    "\n",
    "```\n",
    "Hello from vae.py!\n",
    "Hello from a6_helper.py!\n",
    "```\n",
    "\n",
    "as well as the last edit time for the file `vae.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5zOOPYIUjCUO"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(GOOGLE_DRIVE_PATH)\n",
    "\n",
    "import time, os\n",
    "os.environ[\"TZ\"] = \"US/Eastern\"\n",
    "time.tzset()\n",
    "\n",
    "from vae import hello_vae\n",
    "hello_vae()\n",
    "\n",
    "from a6_helper import hello_helper\n",
    "hello_helper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JuIiv2bhjFoC"
   },
   "source": [
    "Load several useful packages that are used in this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "sLdT7GSljI0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello from vae.py!\n",
      "Hello from a6_helper.py!\n"
     ]
    }
   ],
   "source": [
    "from vae import hello_vae\n",
    "hello_vae()\n",
    "\n",
    "from a6_helper import hello_helper\n",
    "hello_helper()\n",
    "from eecs598.grad import rel_error\n",
    "from eecs598.utils import reset_seed\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import sampler\n",
    "import torchvision.datasets as dset\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# for plotting\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_nqWhiLojS8M"
   },
   "source": [
    "We will use GPUs to accelerate our computation in this notebook. Run the following to make sure GPUs are enabled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "RdQhVgi5jVQp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good to go!\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "  print('Good to go!')\n",
    "else:\n",
    "  print('Please set GPU via Edit -> Notebook Settings.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bcqRQILRjchz"
   },
   "source": [
    "## Load MNIST Dataset\n",
    "\n",
    "\n",
    "VAEs (and GANs as you'll see in the next notebook) are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n",
    "\n",
    "To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mExnwvTXjcF_"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "mnist_train = dset.MNIST('./MNIST_data', train=True, download=True,\n",
    "                           transform=T.ToTensor())\n",
    "loader_train = DataLoader(mnist_train, batch_size=batch_size,\n",
    "                          shuffle=True, drop_last=True, num_workers=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CwDmYBjdhTrM"
   },
   "source": [
    "## Visualize dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2X_21cTwsox"
   },
   "source": [
    "It is always a good idea to look at examples from the dataset before working with it. Let's visualize the digits in the MNIST dataset. We have defined the function `show_images` in `a6_helper.py` that we call to visualize the images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3JMbbxMkwrYg"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 128 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from a6_helper import show_images\n",
    "\n",
    "imgs = loader_train.__iter__().next()[0].view(batch_size, 784)\n",
    "show_images(imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tOdQ3r5diEwr"
   },
   "source": [
    "# Fully Connected VAE\n",
    "\n",
    "Our first VAE implementation will consist solely of fully connected layers. We'll take the `1 x 28 x 28` shape of our input and flatten the features to create an input dimension size of 784. In this section you'll define the Encoder and Decoder models in the VAE class of `vae.py` and implement the reparametrization trick, forward pass, and loss function to train your first VAE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqMjCTSMi0jX"
   },
   "source": [
    "## FC-VAE Encoder\n",
    "\n",
    "Now lets start building our fully-connected VAE network. We'll start with the encoder, which will take our images as input (after flattening C,H,W to D shape) and pass them through a three Linear+ReLU layers. We'll use this hidden dimension representation to predict both the posterior mu and posterior log-variance using two separate linear layers (both shape (N,Z)). \n",
    "\n",
    "Note that we are calling this the 'logvar' layer because we'll use the log-variance (instead of variance or standard deviation) to stabilize training. This will specifically matter more when you compute reparametrization and the loss function later. \n",
    "\n",
    "*Define an `encoder`, `hidden_dim` (H), `mu_layer`, and `logvar_layer` in the initialization of the VAE class in `vae.py`. Use nn.Sequential to define the encoder, and separate Linear layers for the mu and logvar layers. In all of these layers, H will be a hidden dimension you set and will be the same across all encoder and decoder layers. Architecture for the encoder is described below:*\n",
    "\n",
    "\n",
    " * `Flatten` (Hint: nn.Flatten)\n",
    " * Fully connected layer with input size 784 (`input_size`) and output size H\n",
    " * `ReLU`\n",
    " * Fully connected layer with input_size H and output size H\n",
    " * `ReLU`\n",
    " * Fully connected layer with input_size H and output size H\n",
    " * `ReLU`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gTuEAFrgkTyt"
   },
   "source": [
    "## FC-VAE Decoder\n",
    "\n",
    "We'll now define the decoder, which will take the latent space representation and generate a reconstructed image. The architecture is as follows: \n",
    "\n",
    " * Fully connected layer with input size as the latent size (Z) and output size H\n",
    " * `ReLU`\n",
    " * Fully connected layer with input_size H and output size H\n",
    " * `ReLU`\n",
    " * Fully connected layer with input_size H and output size H\n",
    " * `ReLU`\n",
    " * Fully connected layer with input_size H and output size 784 (`input_size`)\n",
    " * `Sigmoid`\n",
    " * `Unflatten` (nn.Unflatten)\n",
    "\n",
    "*Define a `decoder` in the initialization of the VAE class in `vae.py`. Like the encoding step, use `nn.Sequential`*  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aFTb-35TiOZl"
   },
   "source": [
    "## Reparametrization \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SdD23s3Vf70-"
   },
   "source": [
    "Now we'll apply a reparametrization trick in order to estimate the posterior $z$ during our forward pass, given the $\\mu$ and $\\sigma^2$ estimated by the encoder. A simple way to do this could be to simply generate a normal distribution centered at our  $\\mu$ and having a std corresponding to our $\\sigma^2$. However, we would have to backpropogate through this random sampling that is not differentiable. Instead, we sample initial random data $\\epsilon$ from a fixed distrubtion, and compute $z$ as a function of ($\\epsilon$, $\\sigma^2$, $\\mu$). Specifically:\n",
    "\n",
    "$z = \\mu + \\sigma\\epsilon$\n",
    "\n",
    "We can easily find the partial derivatives w.r.t $\\mu$ and $\\sigma^2$ and backpropagate through $z$. If $\\epsilon = \\mathcal{N} (0,1)$, then its easy to verify that the result of our forward pass calculation will be a distribution centered at $\\mu$ with variance $\\sigma^2$.\n",
    "\n",
    "Implement `reparametrization` in `vae.py` and verify your mean and std error are at or less than `1e-4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "T236XnbhbVH4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Error 5.639056398351415e-05\n",
      "Std Error 7.1412955526273885e-06\n"
     ]
    }
   ],
   "source": [
    "reset_seed(0)\n",
    "from vae import reparametrize\n",
    "latent_size = 15\n",
    "size = (1, latent_size)\n",
    "mu = torch.zeros(size)\n",
    "logvar = torch.ones(size)\n",
    "\n",
    "z = reparametrize(mu, logvar)\n",
    "\n",
    "expected_mean = torch.FloatTensor([-0.4363])\n",
    "expected_std = torch.FloatTensor([1.6860])\n",
    "z_mean = torch.mean(z, dim=-1)\n",
    "z_std = torch.std(z, dim=-1)\n",
    "assert z.size() == size\n",
    "\n",
    "print('Mean Error', rel_error(z_mean, expected_mean))\n",
    "print('Std Error', rel_error(z_std, expected_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XOfC7oDrkUhl"
   },
   "source": [
    "## FC-VAE Forward\n",
    "\n",
    "Complete the VAE class by writing the forward pass. The forward pass should pass the input image through the encoder to calculate the estimation of mu and logvar, reparametrize to estimate the latent space z, and finally pass z into the decoder to generate an image.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IQ0UXWyMi9Gu"
   },
   "source": [
    "## Loss Function\n",
    "\n",
    "Before we're able to train our final model, we'll need to define our loss function. As seen below, the loss function for VAEs contains two terms: A reconstruction loss term (left) and KL divergence term (right). \n",
    "\n",
    "$-E_{Z~q_{\\phi}(z|x)}[log p_{\\theta}(x|z)] + D_{KL}(q_{\\phi}(z|x), p(z)))$\n",
    "\n",
    "Note that this is the negative of the variational lowerbound shown in lecture--this ensures that when we are minimizing this loss term, we're maximizing the variational lowerbound. The reconstruction loss term can be computed by simply using the binary cross entropy loss between the original input pixels and the output pixels of our decoder (Hint: `nn.functional.binary_cross_entropy`). The KL divergence term works to force the latent space distribution to be close to a prior distribution (we're using a standard normal gaussian as our prior).\n",
    "\n",
    "To help you out, we've derived an unvectorized form of the KL divergence term for you.\n",
    "Suppose that $q_\\phi(z|x)$ is a $Z$-dimensional diagonal Gaussian with mean $\\mu_{z|x}$ of shape $(Z,)$ and standard deviation $\\sigma_{z|x}$ of shape $(Z,)$, and that $p(z)$ is a $Z$-dimensional Gaussian with zero mean and unit variance. Then we can write the KL divergence term as:\n",
    "\n",
    "$D_{KL}(q_{\\phi}(z|x), p(z))) = -\\frac{1}{2} \\sum_{j=1}^{J} (1 + log(\\sigma_{z|x}^2)_{j} - (\\mu_{z|x})^2_{j} - (\\sigma_{z|x})^2_{j}$)\n",
    "\n",
    "It's up to you to implement a vectorized version of this loss that also operates on minibatches.\n",
    "You should average the loss across samples in the minibatch.\n",
    "\n",
    "Implement `loss_function` in `vae.py` and verify your implementation below. Your relative error should be less than or equal to `1e-5`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vF2ZUj2FjrFa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss error 2.1297676389877955e-06\n"
     ]
    }
   ],
   "source": [
    "from vae import loss_function\n",
    "size = (1,15)\n",
    "\n",
    "image = torch.sigmoid(torch.FloatTensor([[2,5], [6,7]]).unsqueeze(0).unsqueeze(0))\n",
    "image_hat = torch.sigmoid(torch.FloatTensor([[1,10], [9,3]]).unsqueeze(0).unsqueeze(0))\n",
    "\n",
    "expected_out = torch.tensor(8.5079)\n",
    "mu, logvar = torch.ones(size), torch.zeros(size)\n",
    "out = loss_function(image, image_hat, mu, logvar)\n",
    "print('Loss error', rel_error(expected_out,out))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wV8fbzenkAXm"
   },
   "source": [
    "\n",
    "## Train a model\n",
    "\n",
    "Now that we have our VAE defined and loss function ready, lets train our model! Our training script is provided  in `a6_helper.py`, and we have pre-defined an Adam optimizer, learning rate, and # of epochs for you to use. \n",
    "\n",
    "Training for 10 epochs should take ~2 minutes and your loss should be less than 120."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "rWaaacNHsfao"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 \tLoss: 155.725708\n",
      "Train Epoch: 1 \tLoss: 140.303497\n",
      "Train Epoch: 2 \tLoss: 129.399475\n",
      "Train Epoch: 3 \tLoss: 124.136383\n",
      "Train Epoch: 4 \tLoss: 122.571457\n",
      "Train Epoch: 5 \tLoss: 117.879318\n",
      "Train Epoch: 6 \tLoss: 114.999626\n",
      "Train Epoch: 7 \tLoss: 114.248566\n",
      "Train Epoch: 8 \tLoss: 112.982430\n",
      "Train Epoch: 9 \tLoss: 108.438133\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "latent_size = 15\n",
    "from vae import VAE\n",
    "from a6_helper import train_vae\n",
    "input_size = 28*28\n",
    "device = 'cuda'\n",
    "vae_model = VAE(input_size, latent_size=latent_size)\n",
    "vae_model.cuda()\n",
    "for epoch in range(0, num_epochs):\n",
    "  train_vae(epoch, vae_model, loader_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JT6Ek-26jjJD"
   },
   "source": [
    "## Visualize results\n",
    "\n",
    "After training our VAE network, we're able to take advantage of its power to generate new training examples. This process simply involves the decoder: we intialize some random distribution for our latent spaces z, and generate new examples by passing these latent space into the decoder. \n",
    "\n",
    "Run the cell below to generate new images! You should be able to visually recognize many of the digits, although some may be a bit blurry or badly formed. Our next model will see improvement in these results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "RhhrsgrMTyTi"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = torch.randn(10, latent_size).to(device='cuda')\n",
    "import matplotlib.gridspec as gridspec\n",
    "vae_model.eval()\n",
    "samples = vae_model.decoder(z).data.cpu().numpy()\n",
    "\n",
    "fig = plt.figure(figsize=(10, 1))\n",
    "gspec = gridspec.GridSpec(1, 10)\n",
    "gspec.update(wspace=0.05, hspace=0.05)\n",
    "for i, sample in enumerate(samples):\n",
    "  ax = plt.subplot(gspec[i])\n",
    "  plt.axis('off')\n",
    "  ax.set_xticklabels([])\n",
    "  ax.set_yticklabels([])\n",
    "  ax.set_aspect('equal')\n",
    "  plt.imshow(sample.reshape(28,28), cmap='Greys_r')\n",
    "  # plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'vae_generation.jpg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sx3HGSpXk1MY"
   },
   "source": [
    "## Latent Space Interpolation\n",
    "\n",
    "As a final visual test of our trained VAE model, we can perform interpolation in latent space. We generate random latent vectors $z_0$ and $z_1$, and linearly interplate between them; we run each interpolated vector through the trained generator to produce an image.\n",
    "\n",
    "Each row of the figure below interpolates between two random vectors. For the most part the model should exhibit smooth transitions along each row, demonstrating that the model has learned something nontrivial about the underlying spatial structure of the digits it is modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "XZ_4XsFURmN1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 144 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = 12\n",
    "latent_size = 15\n",
    "device = 'cuda'\n",
    "z0 = torch.randn(S,latent_size , device=device)\n",
    "z1 = torch.randn(S, latent_size, device=device)\n",
    "w = torch.linspace(0, 1, S, device=device).view(S, 1, 1)\n",
    "z = (w * z0 + (1 - w) * z1).transpose(0, 1).reshape(S * S, latent_size)\n",
    "x = vae_model.decoder(z)\n",
    "show_images(x.data.cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wzS_ufzEkhah"
   },
   "source": [
    "# Conditional FC-VAE \n",
    "\n",
    "The second model you'll develop will be very similar to the FC-VAE, but with a slight conditional twist to it. We'll use what we know about the labels of each MNIST image, and *condition* our latent space and image generation on the specific class. Instead of $q_{\\phi} (z|x)$ and $p_{\\phi}(x|z)$ we have $q_{\\phi} (z|x,c)$  and $p_{\\phi}(x|z, c)$\n",
    "\n",
    "This will allow us to do some powerful conditional generation at inference time. We can specifically choose to generate more 1s, 2s, 9s, etc. instead of simply generating new digits randomly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hle0JuhwklKc"
   },
   "source": [
    "## Define Network with class input\n",
    "\n",
    "Our CVAE architecture will be the same as our FC-VAE architecture, except we'll now add a one-hot label vector to both the x input (in our case, the flattened image dimensions) and the z latent space. \n",
    "\n",
    "If our one-hot vector is called `c`, then `c[label] = 1` and `c = 0` elsewhere.\n",
    "\n",
    "For the `CVAE` class in `vae.py` use the same FC-VAE architecture implemented in the last network with the following modifications:\n",
    "\n",
    "1. Modify the first linear layer of your `encoder` to take in not only the flattened input image, but also the one-hot label vector `c`\n",
    "2. Modify the first layer of your `decoder` to project the latent space + one-hot vector to the `hidden_dim`\n",
    "3. Lastly, implement the `forward` pass to combine the flattened input image with the one-hot vectors (`torch.cat`) before passing them to the `encoder` and combine the latent space with the one-hot vectors (`torch.cat`) before passing them to the `decoder`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUzKyFI9kp8i"
   },
   "source": [
    "## Train model\n",
    "\n",
    "Using the same training script, let's now train our CVAE! \n",
    "\n",
    "Training for 10 epochs should take ~2 minutes and your loss should be less than 120."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "N1dzKDUsunbD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 \tLoss: 136.369553\n",
      "Train Epoch: 1 \tLoss: 126.592209\n",
      "Train Epoch: 2 \tLoss: 119.267242\n",
      "Train Epoch: 3 \tLoss: 116.810226\n",
      "Train Epoch: 4 \tLoss: 116.206337\n",
      "Train Epoch: 5 \tLoss: 110.515587\n",
      "Train Epoch: 6 \tLoss: 109.168167\n",
      "Train Epoch: 7 \tLoss: 115.046745\n",
      "Train Epoch: 8 \tLoss: 109.072609\n",
      "Train Epoch: 9 \tLoss: 110.909424\n"
     ]
    }
   ],
   "source": [
    "from vae import CVAE\n",
    "num_epochs = 10\n",
    "latent_size = 15\n",
    "from a6_helper import train_vae\n",
    "input_size = 28*28\n",
    "device = 'cuda'\n",
    "\n",
    "cvae = CVAE(input_size, latent_size=latent_size)\n",
    "cvae.cuda()\n",
    "for epoch in range(0, num_epochs):\n",
    "  train_vae(epoch, cvae, loader_train, cond=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GMAyFBZTkr1Y"
   },
   "source": [
    "## Visualize Results\n",
    "\n",
    "We've trained our CVAE, now lets conditionally generate some new data! This time, we can specify the class we want to generate by adding our one hot matrix of class labels. We use `torch.eye` to create an identity matrix, gives effectively gives us one label for each digit. When you run the cell below, you should get one example per digit. Each digit should be reasonably distinguishable (it is ok to run this cell a few times to save your best results).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "GCfwpz0NALdZ"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = torch.randn(10, latent_size)\n",
    "c = torch.eye(10, 10) # [one hot labels for 0-9]\n",
    "import matplotlib.gridspec as gridspec\n",
    "z = torch.cat((z,c), dim=-1).to(device='cuda')\n",
    "cvae.eval()\n",
    "samples = cvae.decoder(z).data.cpu().numpy()\n",
    "\n",
    "fig = plt.figure(figsize=(10, 1))\n",
    "gspec = gridspec.GridSpec(1, 10)\n",
    "gspec.update(wspace=0.05, hspace=0.05)\n",
    "for i, sample in enumerate(samples):\n",
    "  ax = plt.subplot(gspec[i])\n",
    "  plt.axis('off')\n",
    "  ax.set_xticklabels([])\n",
    "  ax.set_yticklabels([])\n",
    "  ax.set_aspect('equal')\n",
    "  plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n",
    "  # plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'conditional_vae_generation.jpg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vW8GmNSwY5Jx"
   },
   "source": [
    "## Final Check\n",
    "\n",
    "Make sure all your training results (loss + images) are saved in the notebook. You can run \"Runtime -> Restart and run all...\" to double check before submitting"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "variational_autoencoders.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3.8.12 ('myenv')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "de1e89af3ef8b17e93d79e53c27fdd6267335e03ee420ef3296d6319bf4dd9be"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
